擅长:python、mysql、java
<p>假设下面是您的列表,那么下面的方法就可以了:</p>
<pre><code>In [192]:
l=[['1810569', 'a', 5, '1241.52'],
['1437437', 'a', 5, '1123.90'],
['1437437', 'b', 5, '1232.43'],
['1810569', 'b', 5, '1321.31'],
['1810569', 'a', 5, '1993.52']]
l
Out[192]:
[['1810569', 'a', 5, '1241.52'],
['1437437', 'a', 5, '1123.90'],
['1437437', 'b', 5, '1232.43'],
['1810569', 'b', 5, '1321.31'],
['1810569', 'a', 5, '1993.52']]
In [201]:
# construct the df and convert the last column to float
df = pd.DataFrame(l, columns=['household ID', 'Member ID', 'some col', 'weights'])
df['weights'] = df['weights'].astype(float)
df
Out[201]:
household ID Member ID some col weights
0 1810569 a 5 1241.52
1 1437437 a 5 1123.90
2 1437437 b 5 1232.43
3 1810569 b 5 1321.31
4 1810569 a 5 1993.52
</code></pre>
<p>因此,我们现在可以<code>groupby</code>在家庭和成员id上,并在“权重”列中调用<code>sum</code>:</p>
^{pr2}$